The Ethical Dimensions of Algorithmic Management: A Qualitative Systematic Review of AI-Based HR Practices, Employee Engagement, and Performance Outcomes

Abstract

Algorithmic management is rapidly mediating artificial intelligence (AI) based human resource (HR) methods such as performance monitoring, algorithmic scheduling, and data-driven workplace appraisal and determining what employees experience as fair, legitimate and manageable in their daily tasks. Despite significant quantitative literature on operational efficiencies, employees' qualitative navigation of algorithmic governance is still unknown. This study is a systematic review of empirical qualitative studies on algorithmic management ethics and employee engagement and performance modifications. Data were searched from 2019 to 2026 in Scopus, Web of Science, CINAHL, and Business Source Complete. Inclusion criteria were: qualitative and mixed methods of data collection and analysis; studies conducted with employees and workers subjected to algorithmic management or AI-based HR practices; the scope of the primary studies covered experiences of algorithmic surveillance, automated evaluations, ethical tensions and performance adaptations; published in English in academic journals between 2019 and 2026. 16 studies in total were used in the final analysis. The papers that were included were mostly of high quality. All 16 studies fully met the CASP criteria. The findings of the 16 included studies were categorised into the theme classifications of: i) structural metric opacity and information asymmetry, ii) the autonomy paradox and grievance suppression, iii) adaptive performance and gaming strategies, and iv) institutional alienation versus sociotechnical legitimacy. Subsequently, managing employee performance modifications includes compliant effort, "work games," quiet resignation, and algorithmic resistance.